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"... There has been a proliferation of proposed mental modules in an attempt to account for different cognitive functions but so far there has been no successful account of their integration. ACT-R (Anderson & Lebiere, 1998) has evolved into a theory that consists of multiple modules but also explain ..."

There has been a proliferation of proposed mental modules in an attempt to account for different cognitive functions but so far there has been no successful account of their integration. ACT-R (Anderson &amp; Lebiere, 1998) has evolved into a theory that consists of multiple modules but also explains how they are integrated to produce coherent cognition. The perceptual-motor modules, the goal module, and the declarative memory module are presented as examples of specialized systems in ACT-R. These modules are associated with distinct cortical regions. These modules place chunks in buffers where they can be detected by a production system that responds to patterns of information in the buffers. At any point in time a single production rule is selected to respond to the current pattern. Subsymbolic processes serve to guide the selection of rules to fire as well as the internal operations of some modules. Much of learning involves tuning of these subsymbolic processes. Empirical examples are presented that illustrate the predictions of ACT-R’s modules. In addition, two models of complex tasks are described to illustrate how these modules result in strong predictions when they are brought together. One of these models is concerned with complex patterns of behavioral data in a dynamic task and the other is concerned with fMRI data obtained in a study of symbol manipulation.

"... The time course of perceptual choice is discussed in a model based on gradual and stochastic accumulation of information in non-linear decision units with leakage (or decay of activation) and competition through lateral inhibition. In special cases, the model becomes equivalent to a classical diffus ..."

The time course of perceptual choice is discussed in a model based on gradual and stochastic accumulation of information in non-linear decision units with leakage (or decay of activation) and competition through lateral inhibition. In special cases, the model becomes equivalent to a classical diffusion process, but leakage and mutual inhibition work together to address several challenges to existing diffusion, random-walk, and accumulator models. The model provides a good account of data from choice tasks using both time-controlled (e.g., deadline or response signal) and standard reaction time paradigms and its overall adequacy compares favorably with that of other approaches. An experimental paradigm that explicitly controls the timing of information supporting different choice alternatives provides further support. The model captures flexible choice behavior regardless of the number of alternatives, accounting for the linear slowing of reaction time as a function of the log of the number of alternatives (Hick’s law) and explains a complex pattern of visual and contextual priming effects in visual word identification. Perceptual Choice 2 When an experience presents itself to the senses, the need often arises to determine its identity or to make some other judgment about it. In experimental paradigms, the time course of this judgment process is

...ro's claim was incorrect. Given this finding, we know of no empirical evidence against the possibility that recurrence plays a role in perception. 554 USHER AND McCLELLAND Dunbar, & McClelland, 1990; =-=Plaut, McClelland, Seidenberg, & Patterson, 1996-=-; Seidenberg & McClelland, 1989), there have been some drawbacks to this approach. First, it has tended to distance the work from insights from neuroscience that might inform and constrain it (Grossbe...

"... Two connectionist frameworks, GRAIN (McClelland, 1993) and BSB (Anderson, 1991), and the diffusion model (Ratcliff, 1978) were evaluated using data from a signal detection task. Subjects were asked to choose one of two possible responses to a stimulus and were provided feedback about whether the cho ..."

Two connectionist frameworks, GRAIN (McClelland, 1993) and BSB (Anderson, 1991), and the diffusion model (Ratcliff, 1978) were evaluated using data from a signal detection task. Subjects were asked to choose one of two possible responses to a stimulus and were provided feedback about whether the choice was correct. The dependent variables included response probabilities, reaction times for correct and error responses, and reaction time distributions, and the independent variables were stimulus value, stimulus probability, and lag from an abrupt switch in stimulus probability. The diffusion model accounted for all aspects of the asymptotic data, including error reaction times, which had previously been a problem. The connectionist models accounted for many aspects of the data adequately, but each failed to a greater or lesser degree in important ways except for one model very similar to the diffusion model. The connectionist learning mechanisms were unable to account for initial learning or abrupt changes in stimulus probability. The results provide an advance in the development of the diffusion model and show that the long tradition of reaction time research and theory is a fertile domain for development and testing of connectionist assumptions about how decisions are generated over time.

"... We present statistical analyses of the large-scale structure of three types of semantic networks: word associations, WordNet, and Roget's thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path-lengths between words, and strong local ..."

We present statistical analyses of the large-scale structure of three types of semantic networks: word associations, WordNet, and Roget&apos;s thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path-lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale-free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities have also been found in certain other complex natural networks, such as the world wide web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high-dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small-world statistics and power-law connectivity distributions, and also suggests one possible mechanistic basis for the effects of learning history variables (age-ofacquisition, usage frequency) on behavioral performance in semantic processing tasks.

...t might activate nodes with higher connectivity more quickly, much as some connectionist modelers have suggested that retrieving traces with lower error could be faster for a number of reasons (e.g., =-=Plaut, McClelland, Seidenberg, & Patterson, 1996-=-). Mechanistically, a bias for retrieving high-connectivity nodes first could arise naturally if memory search is implemented by some kind of serial or parallel Markov process operating on a semantic ...

"... An interactive 2-step theory of lexical retrieval was applied to the picture-naming error patterns of aphasic and nonaphasic speakers. The theory uses spreading activation in a lexical network to accomplish the mapping between the conceptual representation of an object and the phonological form of t ..."

An interactive 2-step theory of lexical retrieval was applied to the picture-naming error patterns of aphasic and nonaphasic speakers. The theory uses spreading activation in a lexical network to accomplish the mapping between the conceptual representation of an object and the phonological form of the word naming the object. A model developed from the theory was parameterized to fit normal error patterns. It was then &amp;quot;lesioned &amp;quot; by globally altering its connection weight, decay rates, or both to provide fits to the error patterns of 21 fluent aphasic patients. These fits were then used to derive predictions about the influence of syntactic categories on patient errors, the effect of phonology on semantic errors, error patterns after recovery, and patient performance on a single-word repetition task. The predictions were confirmed. It is argued that simple quantitative alterations to a normal processing model can explain much of the variety among patient patterns in naming. Difficulty in word retrieval is the most pervasive symptom of language breakdown in aphasia. As with other symptoms of brain damage, word retrieval is subject to graceful degradation (Marr, 1982; Rumelhart &amp; McClelland, 1986): Unsuccessful attempts at retrieval generally resemble the target, either in

"... The development of reading skill and bases of developmental dyslexia were explored using connectionist models. Four issues were examined: the acquisition of phonological knowledge prior to reading, how this knowledge facilitates learning to read, phonological and non phonological bases of dyslexia, ..."

The development of reading skill and bases of developmental dyslexia were explored using connectionist models. Four issues were examined: the acquisition of phonological knowledge prior to reading, how this knowledge facilitates learning to read, phonological and non phonological bases of dyslexia, and effects of literacy on phonological representation. Compared with simple feedforward networks, representing phonological knowledge in an attractor network yielded improved learning and generalization. Phonological and surface forms of developmental dyslexia, which are usually attributed to impairments in distinct lexical and nonlexical processing “routes,” were derived from different types of damage to the network. The results provide a computationally explicit account of many aspects of reading acquisition using connectionist principles.

"... We present a theoretical framework for understanding the roles of the hippocampus and neocortex in learning and memory. This framework incorporates a theme found in many theories of hippocampal function, that the hippocampus is responsible for developing conjunctive representations binding together ..."

We present a theoretical framework for understanding the roles of the hippocampus and neocortex in learning and memory. This framework incorporates a theme found in many theories of hippocampal function, that the hippocampus is responsible for developing conjunctive representations binding together stimulus elements into a unitary rep- resentation that can later be recalled from partial input cues. This idea appears problematic, however, because it is contradicted by the fact that hippocampally lesioned rats can learn nonlinear discrimination problems that require conjunctive representations. Our framework accommodates this finding by establishing a principled division of labor between the cortex and hippocampus, where the cortex is responsible for slow learning that integrates over multiple experiences to extract generalities, while the hippocampus performs rapid learning of the arbitrary contents of individual experiences. This framework shows that nonlinear discrimination problems are not good tests of hippocampal function, and suggests that tasks involving rapid, incidental conjunctive learning are better. We implement this framework in a computational neural network model, and show that it can account for a wide range of data in animal learning, thus validating our theoretical ideas, and providing a number of insights and predictions about these learning phenomena.

...s. Furthermore, we also note that models of slow, integrative cortical learning are capable of demonstrating flexibility in the form of generalizing to novel inputs (e.g., pronouncing novel nonwords; =-=Plaut, McClelland, Seidenberg, & Patterson, 1996-=-). Indeed, one of the primary advantages of this slow, integrative learning is that it facilitates generalization based on the regularities extracted from a large number of prior experiences. Thus, th...

...hods into account. To explore this question, Hutzler, Ziegler, Perry, Wimmer, and Zorzi (2004) compared the performance of two major connectionist reading models in two languages, the triangle model (=-=Plaut, McClelland, Seidenberg, & Patterson, 1996-=-) and the two-layer associative model (Zorzi, Houghton, & Butterworth, 1998a, 1998b). These models were 21s22 trained on a comparable database of German and English words and were tested on an identic...

"... Several previous studies have suggested that basic decoding skills may develop less effectively in English than in some other European orthographies. The origins of this effect in the early (foundation) phase of reading acquisition are investigated through assessments of letter knowledge, familiar w ..."

Several previous studies have suggested that basic decoding skills may develop less effectively in English than in some other European orthographies. The origins of this effect in the early (foundation) phase of reading acquisition are investigated through assessments of letter knowledge, familiar word reading, and simple nonword reading in English and 12 otherorthographies. The results conrm that children from a majority of European countries become accurate and uent in foundation level reading before the end of the rst school year. There are some exceptions, notably in French, Portuguese, Danish, and, particularly, in English. The effects appear not to be attributable to differences in age of starting or letter knowledge. It is argued that fundamental linguistic differences in syllabic complexity and orthographic depth are responsible. Syllabic complexity selectively affects decoding, whereas orthographic depth affects both word reading and nonword reading. The rate of development in English is more than twice as slow as in the shallow orthographies. It is hypothesized that the deeper orthographies induce the implementation of a dual (logographic+ alphabetic) foundation which takes more than twice as long to establish as the single foundation required for the learning of

...92, 1997). In their turn, the foundations underpin the development of an orthographic framework in which the full complexity of the spelling system is represented in an abstract generalizable format (=-=Plaut, McClelland, Seidenberg, & Patterson, 1996-=-). Reading acquisition is 144 Philip H. K. Seymour et al. Figure 1. Schematic representation of the dual foundation model of orthographic development (from Duncan & Seymour, 2000). paralleled by devel...

"... A computational model of human memory for serial order is described (OSCillator-based Associative Recall [OSCAR]). In the model, successive list items become associated to successive states of a dynamic learning-context signal. Retrieval involves reinstatement of the learning context, successive sta ..."

A computational model of human memory for serial order is described (OSCillator-based Associative Recall [OSCAR]). In the model, successive list items become associated to successive states of a dynamic learning-context signal. Retrieval involves reinstatement of the learning context, successive states of which cue successive recalls. The model provides an integrated account of both item memory and order memory and allows the hierarchical representation of temporal order information. The model accounts for a wide range of serial order memory data, including differential item and order memory, transposition gradients, item similarity effects, the effects of item lag and separation in judgments of relative and absolute recency, probed serial recall data, distinctiveness effects, grouping effects at various temporal resolutions, longer term memory for serial order, list length effects, and the effects of vocabulary size on serial recall. The serial ordering of behavior is central to much, perhaps most, of human cognition (e.g., Lashley, 1951). Studies of memory for serial order have provided rich data on the psychological repre-sentation of serial order information and therefore offer a signifi-cant challenge to any model of serially ordered behavior. In this

...llel redintegrative processes can be implemented using distributed item representations in a more complete architecture (see Chappell & Humphreys, 1994; Lewandowsky, in press; Lewandowsky & Li, 1994; =-=Plaut, McClelland, Seidenberg, & Patterson, 1996-=-). We illustrate the learning of a list of five items with a simple version of the model in which there are relatively few parameters. Additional parameters are introduced as necessary throughout the ...